Comments for MEDB 5501, Week 7

Two-sample t-test (Independent-samples t-test)

Randomized trial
- Convenience sample
- Random assignment to treatment, control
- Measure continuous outcome
Cohort design
- Observe exposed and control subjects
- No random assignment
- Measure continuous outcome

Assumptions

Group 1, 2 both normally distributed
- Assessed with histograms, boxplots, Q-Q plots
- Or rely on Central Limit Theorem
Possibly different means, but same variance
- Assessed with boxplot, descriptive statistics
- Also Levene’s test (not recommended)
Observations are independent
- Between groups
- Within groups
- Assessed qualitatively

Normality

Assess each group separately, or
Combine after subtracting means
Less concern with normality for large sample sizes

Equal variances (homescedascity)

Compare the box part of the box plots
- Look for large disparities only (2 or 3 fold)
Calculate and compare the standard deviations
- Again, large disparities only
Levene’s test (not recommended)
- Too little power for small sample sizes
- Too much power for large sample sizes
- Very sensitive to normality assumption

Independence

Assessed qualitatively
Independence between groups
- No matching
- No longitudinal measures
Independence within groups
- No cluster effects
- No infectious spread

Independence is almost always assessed qualitatively. You have to assume indpendence between the two groups first. Data that is matched or paired violates this assumption (see the paired t-test later). You also can’t use the same subjects again. If you want to compare the same subjects at different time points that would be a violation of independeance.

You also have to assume independence within groups. I was doing a study of breast feeding where the researchers recruited 84 infants. There were 72 singleton births and 6 sets of twins. I bet the researchers were excited to get these twins: two observations for the price of a single consent form. But the measurement of breast feeding duration is not going to be independent. If one baby in the twin pair stops breast feeding, it increases the chances that the other baby will stop at the same time. It doesn’t happen all the time, but it happens enough that you can’t say that the data are independent.

Any time you have a clustering effect, measurements in the same family, for example, you should probably assume that the outcomes are not independent. If you conduct studies at multiple clinics, the observations of two patients at the same clinic are not going to be independent because the standard of care, skill of the medical personnel, and patient referral patterns are going to influence the results.

It’s okay if only a single clinic evaluates all of the patients. It’s only when there are multiple clinics that this may become an issue.

If you have clusters in your data, you can still analyze the data, but you have to account for the clusters in the model. Otherwise, the confidence intervals and hypothesis tests will be distorted.

Housing data dictionary, 1 of 5

source: 
  This file was found originally at a website 
  DASL (Data And Story Library) that is no 
  longer available. 

description:  
  The original source describes the data as
  "a random sample of records of resales of 
  homes from Feb 15 to Apr 30, 1993 from the
  files maintained by the Albuquerque Board 
  of Realtors. This type of data is 
  collected by multiple listing agencies in
  many cities and is used by realtors as an
  information base."

Housing data dictionary, 2 of 5

copyright:  
    Unknown. You should be able to use this data for
  individual educational purposes under the Fair Use
  guidelines of U.S. copyright law.

format: 
  delimiter: space
  varnames: first row of data
  missing-value-code: *
  rows: 117
  columns: 8

Housing data dictionary, 3 of 5

vars:
  Price:
    label: Selling price
    unit: dollars
    
  SquareFeet:
    label: Living space
    unit: square feet
    
  AgeYears:
    label: Age of home
    unit: years

Housing data dictionary, 4 of 5

  NumberFeatures:
    label: 
      Home features (dishwasher, refrigerator,
      microwave, disposer, washer, intercom, 
      skylight(s), compactor, dryer, handicap
      fit, cable TV access)
    scale: count
    range: 0 to 11  
    
  Northeast:
    label: Located in northeast sector of city?
    values:
      Yes: 1
      No: 0

Housing data dictionary, 5 of 5

  CustomBuild:
    label: Custom built?
    values:
      Yes: 1
      No: 0
    
  CornerLot:
    label: Corner location?
    values:
      Yes: 1
      No: 0

  Tax:
    label: Yearly property tax
    unit: dollars

Housing analysis

Sample size calculation

Moon data dictionary (1/5)

---
data_dictionary: moon.txt

source:
  This data file is part of OzDASL, an archive of various data sets 
  useful for teaching. The maintainers of the data archive are from
  Australia, but this particular data set is not specific to that part
  of the world. The entire archive is at
  https://dasl.datadescription.com/

Moon data dictionary (1/5)

description:
  This data set shows a perceptual experiment where subjects were asked
  to estimate a size ratio with their head level to the ground and then
  with their head elevated (in other words, looking upward). Although
  the objects being compared were the same size, almost all subjects 
  overestimated the relative sizes. The hypothesis to be tested is 
  whether the overestimation is greater with eyes level than with eyes
  elevated. A more detailed description is available at
  https://gksmyth.github.io/ozdasl/general/moon.html
  
download:
  https://gksmyth.github.io/ozdasl/general/moon.txt

Moon data dictionary (1/5)

copyright:  
  Unknown. You should be able to use this data for individual 
  educational purposes under the Fair Use guidelines of U.S. 
  copyright law.

format: 
  delimiter: tab
  varnames: first row of data
  missing-value-code: not needed
  rows: 10
  columns: 3

Moon data dictionary (1/5)

vars:
  Subject:
    label: Subject number
    format: numeric

  Elevated:
    label: Perceived ratio with eyes elevated
    format: numeric
    
  Level:
    label: Perceived ratio with eyes level
    format: numeric
---